I mean, it's hard to say what the mainstream says, especially cause it's the biggest, and currently in a collapsing concensus more empiricism until the storm passes phase.
But I don't think adding more caviets to the bad old stories is going to cut it. People need narratives, and if you say "supply curve up, demand curve down, many astrices" you are biasing a certain simplification when people inevitably need narratives.
Really what we need to do is run more big policy experiments at the fed. The more wild policy swings there are, more more politics feels alive, and better are empiracism is. The micro empiracism today is good but barely econ. (It's sort of abstract social science do a tiny thing and then stats.) The macro empiracism is still frequently quite bad, where better data would help but clearer trends from legit experiments would help even more.
I know research is hard, and these issues are fraught with complexities and challenges in acquiring data, but it still boggles my mind just how many macro economic theories don't have strong empirical data to support them.
This. If you look at the right ideas of Macro 101, the success rate is pretty high. Bailouts, demand-side policy, properly applied monetary stimulus, expectations management and confidence. If you look at famous wrong ideas, their failure rate has been pretty high. Austerity, strong currencies, lazy ZIRP stimulus, do-nothing policy, inflation paranoia. What is true in macro before the recession has become more true. The econ pundits who focused on these Macro 101 truths have been mostly right, with the monetary guys being perhaps a little more right than the fiscal stimulus guys. This should not be surprising as the things that are "true" in econ are such by virtue of hundreds of years of examination and highly reasonable philosophy, which anyone who's bothered to study econ would know.
Most people who say "econ is not a science" have had little to no experience with people who conduct economics as a science. I understand the cargo-cultists and pundits are much louder voices, but someone good at "science" shouldn't lazily examine the most shallow voices and use that to conclude deep things about a field of study.
There's cargo cult science, and then there's cargo cult scientific criticism.
Not a bad article, but it doesn't go far enough. Most macroeconomists use false assumptions and bad models to make terrible predictions. It's hard to accept that a field like this can be dominated by people making the most basic of errors, so many observers can't bring themselves to reject these methods wholesale.
What I'm calling for is for economists to drop the pretense and misconception that using empirical methods for studying macroeconomics makes it scientific. And I'm suggesting that being good at math is not useful unless you're using useful data. In the study of logic, arguments can be considered valid if they are formally correct, but still unsound if their premises are false. Much in the same way, one can perform any number of valid mathematical transformations on data but still be left with unsound conclusions if those data were gathered incorrectly.
I am not saying that empiricism is inherently flawed, or that we should stop collecting economic data. And I I do not intend to advocate any particular school of economic thought here. All I'm advocating is that students be taught how to think critically about what they are being taught. So much of a modern economics education consists of looking at the changes in figures over time that very little is spent focused on a more general kind of reasoning.
The kind of reasoning I'm calling for is not easy to define. This is one of the tremendous advantages numbers have over argument in most minds. This kind of reasoning takes into account the notion that most of the information we obtain is not perfect or complete, and that many of our determinations are really judgment calls on what is more likely to be true. If empiricism is reasoning with your eyes, this is reasoning with your nose. It is a trained skill that allows you to recognize dubious premises and unspoken assumptions. When refined, it allows you to distill the essence of arguments down to a set of axioms that you can use to build a coherent model of the situation at hand. It is this theoretical side that allows you to understand how to construct experiments that test hypotheses, or whether that is even possible in each case.
To demonstrate the importance of gaining an understanding of the theory and rules behind something before testing it, I offer a parable:
The commissioner of the NFL once decided that teams were punting too much and he hired an econometrician (economic statistician) to study the situation and provide a solution to this problem. The econometrician applied his skills to the task at hand, aggregating data from several seasons to find correlations. He noted that there is an incredibly strong correlation between forth downs and punting, and he recommended that the commissioner ban fourth downs. In the next season, offenses were only given three downs. To the econometrician's surprise and the commissioner's chagrin, teams actually punted more frequently, as the fewer number of downs dramatically limited offensive opportunities.
The econometrician's misunderstanding was based on something rather obvious (if you understand American Football): a failure to separate correlation and causation due to an ignorance of the rules of the game. And compared to a global economy, football is a very simple game, with very simple rules. Applying reasoning to the example is very straightforward, but applying the same thing to a world of dynamic human behavior is much more subtle. Which is why students ought to be trained to question assumptions and sense where logic and math have separated themselves from the reality they are supposed to help us describe.
People will disagree about when things correlate to reality, and about what things make sense in parables. But almost anyone can learn to recognize when a number seems too specific, just like most decent coders learn to recognize "code smell." Just the other day, someone told me confidently that 65% of communication is non-verbal. Now, while I almost agree intuitively, I immediately asked where they heard that, and how someone could have arrived at that figure, which seemed oddly specific for something (communication) that I don't think is frequently quantitized. Every student of a soft science needs to have this skill strongly developed, or they will begin to take these kinds of things at face value.
But don't get me wrong, I am not defending current Macro. It has been extremely cool&good that everyone, including economists, now believes that the top-of-the-line models are basically garbage and economists are idiots.
It means that people actually doing econ now are rather humble about what they do, and still have to be extremely technical and smart. Indeed, if you bring in a new theory that works, you'll probably be famous very quickly. The bar is pretty high though, your model gotta be inferential and parameters causally identified, while still fitting the data well.
Nevertheless, my macroeconomist buddies got _really_ excited about doing "that machine learning" pretty much years before other fields were even talking about it (until they found out they basically use the same models already), because everyone is looking for the next big idea all the time.
It's exactly the sort of old-style economists who can explain everything with their always-correct theories and basic math and then claim deference by everyone, who don't really have a place anymore - except writing articles for certain business newspapers, that is.
Ok, I’m sympathetic to that POV. I’m grateful for the credibility revolution in econometrics, but it clearly can become an obsession. Prediction on its own can be worthwhile. I wouldn’t say causation is a myth - maybe rather that it’s overvalued.
I read a lot of economics and policy blogs (Marginal Revolution, Felix Salmon, Brad DeLong, etc...) and have come to the conclusion that the macro side of economics is still so in its infancy that it's can barely be called scientific. What you see is that various ideologies adopt their particular model (Keynsian, Austrian, etc...) and from there, the general lack of empiricism (never possible to run an experiment without confounding variables) plus confirmation bias (all deviations from your model can be explained by special circumstances) fuels a never ending debate.
So I try to remain skeptical of (seemingly polemical) works such as this one.
I think it's better to think of it no as a complete rejection of reality, but recognizing the limitations of empirical methods. Mainstream economics today is arguably too focused on indicators, just collecting a bunch of data and finding correlations, then jumping to causation from that.
There is a whole branch of econometrics that has endless methods of doing macroeconomic quantitative analysis using data. It just doesn't work on the macro level, the data is too aggregated, the system is too entangled with dependencies between collected data and incomparable between countries, results have only historic and not predictive value and even then they are usually more voodoo than anything else.
So I disagree: Macro economics needs less computation, data and abstract mathematical models.
The Hayek quote that CaseySoftware wrote into another toplevel comment sums it up perfectly: Only pretense of knowledge coming from this.
The problem is that the notion that 'microfoundations' can be linked to the macro economy by searching for 'deep parameters' suffers from precisely the same critique.
It's even proved wrong within the body of theory that embraces it. The Sonnenschein-Mantel-Debreu theorem.
Economists then do a lot of hand waving and point to 'data', without acknowledging that the 'data' is collected from a system that has been under the influence of their ideas for over 40 years and therefore will, inevitably, be the right shape.
If you collect data from a man in a straitjacket, unsurprisingly it will look like a man in straitjacket. It can tell you nothing about ideas that involve removing the straitjacket.
Non-falsifiability of theorems is the hallmark of macroeconomics. Because it is an ideology dressed up as a science.
PhD in econometrics speaking, here's my handy guide to macro-economics:
- The main variables of interest are (aggregate) real production / income (GDP/GDI), unemployment and inflation (arguably in that order)
- Nobody knows much wrt the causes, effects and future trajectories of any of these. Not professional Ivy League economists who publish in journals like AER, QJE, JPE or Econometrica, nor traders getting paid millions at hedge funds or prop trading desks, nor fringe bloggers, gold/crypto-bugs or neophytes from different fields (traditionally from physics, but probably increasingly from CS/AI/ML). Especially beware when they sound very sure of themselves, often using correct wonky economic jargon or details like the plumbing of money flows. Top academic economist are at least (usually) somewhat honest that they know very very little.
- Even if someone did know anything, 3rd parties like you or I can't distinguish the Real Truth from quackery.
- The root cause of this knowledge deficiency is the inability to run proper controlled experiments. Pretty much no theory about macro-economics is convincingly testable/falsifiable, except banal trivialities like we can't make everybody rich by sending everyone a $10M check. This will not change in our lifetime, if ever.
- All the writing on macro-economics is story-telling and catering to their specific audiences. Academics write foremost for other academics to gain a position at a prestigious faculty (and incidentally to influence politics). Crypto-bugs write to sell you crypto-coins. Fringe bloggers like Shadowstats write to get newsletter signups and ad-dollars. Most of them employ the effective mechanism that the reader is initiated to advanced/semi-hidden knowledge, which makes the reader feel better about themselves.
Given this, the answer to your question is: maybe, maybe not, who knows?
The biggest problem is figuring out the truth from lies and mistakes and competing narratives. Maybe the pundit predicted that unemployment would go up, and some say it did, but others explain that it's not actually up because people are leaving the workforce voluntarily, others claim that it was miscalculated in the past, yet others claim that it's actually a bad measure that shouldn't be trusted at all.
There are very few directly testable predictions. Even something as basic as whether the Earth is flat is hard to conclusively prove without enough background - how could you hope to understand the economy?
It’s _really easy_ to write down a moderately realistic macro model which you have absolutely no hope of ever solving numerically (an “extremely realistic model” would be even worse). That’s not going to do anyone any good.
It would be a totally empty exercise: “here’s something which I bet would be great but we’ll never know!”. It’s impossible to confront such models with data, which is absolutely the bread and butter of any macro paper.
It is still hard but somewhat less hard to write down a simplified model which you can solve and which can give you insights. By necessity you lose some realism in this exercise. There is no way around that. It’s not a lack of cleverness or imagination.
It is possible to confront such models with data to make the whole exercise empirical, which credible macro is and must be.
By necessity these insights might be useful or not depending on how much of the useful machinery of realism you dropped from your model to make it solvable.
If you personally want something “better”, you are welcome to try. I encourage you! If you show that your method works - that it involves plausible assumptions, can be solved, and can be made empirical - the macroeconomists of the world will beat a path to your door and gladly study your methods.
The exercise not a charade. There are currently no alternatives to mainstream empirical macro bc all of the purported alternatives are (for lack of a better word) complete BS (example: MMT).
You can say that objective reality is dubious. But if you use that to subvert claims that survive testing, you're making a positive statement and you need a testable alternative. In any other case, what you're doing is using philosophy to push ideology by discrediting established facts.
I think we should separate "empiricism here is currently difficult" to "absolutely impossible"
Some fields of social science are undergoing internal turmoil (eg. Macroeconomics and some subfields of psychology). This is a good sign; old models are being discarded as new evidence and better methods pop up. In fact, I'm more concerned about sociology, where there wasn't a replication crisis.
Facts are being improved upon when you see crisis in replication. That's a sign of a healthy field of study.
As an econometrics/labor econ grad student, I can tell you I (and most of macro) could answer many questions definitively if we could do things we absolutely should not (eg. Run RCTs on real cities on things like the minimum wage). In microeconomics, we've resorted mainly to look for the effect of exogenous shocks on systems we care about (eg. The Muriel boatlift made a few studies on local unemployment/immigration due to the exogenous shocks nature of the event)
And we have statistical evidence from large numbers of economies. It's not perfect. We make a best guess and go for it. That's my point.
Contrary to popular belief, these guys aren't winging it. As I've said, go check out the research they're doing at the Fed. That's the cutting edge of economics.
I think the catch with macroeconomics is that anybody can come by after the fact and provide an explanation for why things happened the way they did. Many of the explanations are not falsifiable, and two competing explanations might both sound reasonable. So, a person chooses the explanation they prefer for some reason other than economics. If they distrust government, they prefer the explanation that says it was government's fault. If they hold contempt for the rich, they prefer the explanation of corporate greed. And so on...
One of the key tenants of science is that a hypothesis makes testable predictions. If we can't falsify economic explanations, then perhaps we should take this approach and focus on the theories that make the best predictions.
This is more or less where we are now with mainstream macroeconomics - a previously fringe theory becomes dominant and too many people stop thinking in deference to the experts.
But I don't think adding more caviets to the bad old stories is going to cut it. People need narratives, and if you say "supply curve up, demand curve down, many astrices" you are biasing a certain simplification when people inevitably need narratives.
Really what we need to do is run more big policy experiments at the fed. The more wild policy swings there are, more more politics feels alive, and better are empiracism is. The micro empiracism today is good but barely econ. (It's sort of abstract social science do a tiny thing and then stats.) The macro empiracism is still frequently quite bad, where better data would help but clearer trends from legit experiments would help even more.
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